

'''simple docstring'''





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			str:
					assert isinstance(__UpperCamelCase    ,__UpperCamelCase	)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'    ,[False, True]	)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			List[Any]:
					lowerCamelCase_  				=      tmp_path / 'cache'
					lowerCamelCase_  				=      {'text': 'string'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										lowerCamelCase_  				=      TextDatasetReader(__UpperCamelCase    ,cache_dir=__UpperCamelCase    ,keep_in_memory=__UpperCamelCase	).read()
					_check_text_dataset(__UpperCamelCase    ,__UpperCamelCase	)







@pytest.mark.parametrize(
    'features'    ,[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]    ,)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Union[str, Any]:
					lowerCamelCase_  				=      tmp_path / 'cache'
					lowerCamelCase_  				=      {'text': 'string'}
					lowerCamelCase_  				=      features.copy() if features else default_expected_features
					lowerCamelCase_  				=      (
					    Features({feature: Value(__UpperCamelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					lowerCamelCase_  				=      TextDatasetReader(__UpperCamelCase    ,features=__UpperCamelCase    ,cache_dir=__UpperCamelCase	).read()
					_check_text_dataset(__UpperCamelCase    ,__UpperCamelCase	)







@pytest.mark.parametrize('split'    ,[None, NamedSplit('train'	), 'train', 'test']	)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Union[str, Any]:
					lowerCamelCase_  				=      tmp_path / 'cache'
					lowerCamelCase_  				=      {'text': 'string'}
					lowerCamelCase_  				=      TextDatasetReader(__UpperCamelCase    ,cache_dir=__UpperCamelCase    ,split=__UpperCamelCase	).read()
					_check_text_dataset(__UpperCamelCase    ,__UpperCamelCase	)
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'    ,[str, list]	)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Optional[int]:



					if issubclass(__UpperCamelCase    ,__UpperCamelCase	):
										lowerCamelCase_  				=      text_path
					elif issubclass(__UpperCamelCase    ,__UpperCamelCase	):
										lowerCamelCase_  				=      [text_path]
					lowerCamelCase_  				=      tmp_path / 'cache'
					lowerCamelCase_  				=      {'text': 'string'}
					lowerCamelCase_  				=      TextDatasetReader(__UpperCamelCase    ,cache_dir=__UpperCamelCase	).read()
					_check_text_dataset(__UpperCamelCase    ,__UpperCamelCase	)







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=("train",)	)       ->			List[Any]:
					assert isinstance(__UpperCamelCase    ,__UpperCamelCase	)
					for split in splits:
										lowerCamelCase_  				=      dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'    ,[False, True]	)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			str:
					lowerCamelCase_  				=      tmp_path / 'cache'
					lowerCamelCase_  				=      {'text': 'string'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										lowerCamelCase_  				=      TextDatasetReader({'train': text_path}    ,cache_dir=__UpperCamelCase    ,keep_in_memory=__UpperCamelCase	).read()
					_check_text_datasetdict(__UpperCamelCase    ,__UpperCamelCase	)







@pytest.mark.parametrize(
    'features'    ,[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]    ,)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Tuple:
					lowerCamelCase_  				=      tmp_path / 'cache'
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					lowerCamelCase_  				=      {'text': 'string'}
					lowerCamelCase_  				=      features.copy() if features else default_expected_features
					lowerCamelCase_  				=      (
					    Features({feature: Value(__UpperCamelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					lowerCamelCase_  				=      TextDatasetReader({'train': text_path}    ,features=__UpperCamelCase    ,cache_dir=__UpperCamelCase	).read()
					_check_text_datasetdict(__UpperCamelCase    ,__UpperCamelCase	)







@pytest.mark.parametrize('split'    ,[None, NamedSplit('train'	), 'train', 'test']	)
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			List[str]:



					if split:
										lowerCamelCase_  				=      {split: text_path}
					else:
										lowerCamelCase_  				=      'train'
										lowerCamelCase_  				=      {'train': text_path, 'test': text_path}
					lowerCamelCase_  				=      tmp_path / 'cache'
					lowerCamelCase_  				=      {'text': 'string'}
					lowerCamelCase_  				=      TextDatasetReader(__UpperCamelCase    ,cache_dir=__UpperCamelCase	).read()
					_check_text_datasetdict(__UpperCamelCase    ,__UpperCamelCase    ,splits=list(path.keys()	)	)
					assert all(dataset[split].split == split for split in path.keys()	)




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@slow
			@require_torch
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' ,  '''prajjwal1/bert-tiny'''		)
							__lowercase					     =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

							__lowercase					     =				bertabert.config.encoder.vocab_size
							__lowercase					     =				tokenizer.sep_token_id
							__lowercase					     =				tokenizer.cls_token_id
							__lowercase					     =				1_28

							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''train[:1%]'''		)
							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''validation[:1%]'''		)

							__lowercase					     =				train_dataset.select(range(32		)		)
							__lowercase					     =				val_dataset.select(range(16		)		)

							__lowercase					     =				4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__		):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase					     =				tokenizer(batch['''article'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=5_12		)
											__lowercase					     =				tokenizer(batch['''highlights'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=1_28		)
											__lowercase					     =				inputs.input_ids
											__lowercase					     =				inputs.attention_mask

											__lowercase					     =				outputs.input_ids
											__lowercase					     =				outputs.input_ids.copy()
											__lowercase					     =				[
											    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase					     =				outputs.attention_mask

											assert all(len(lowerCAmelCase__		) == 5_12 for x in inputs.input_ids		)
											assert all(len(lowerCAmelCase__		) == 1_28 for x in outputs.input_ids		)

											return batch

							def _compute_metrics(lowerCAmelCase__		):
											__lowercase					     =				pred.label_ids
											__lowercase					     =				pred.predictions

											# all unnecessary tokens are removed
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

											__lowercase					     =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(lowerCAmelCase__		)		)]		) / len(lowerCAmelCase__		)

											return {"accuracy": accuracy}

							# map train dataset
							__lowercase					     =				train_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							train_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							# same for validation dataset
							__lowercase					     =				val_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							val_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							__lowercase					     =				self.get_auto_remove_tmp_dir()

							__lowercase					     =				SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__ ,  per_device_train_batch_size=lowerCAmelCase__ ,  per_device_eval_batch_size=lowerCAmelCase__ ,  predict_with_generate=lowerCAmelCase__ ,  evaluation_strategy='''steps''' ,  do_train=lowerCAmelCase__ ,  do_eval=lowerCAmelCase__ ,  warmup_steps=0 ,  eval_steps=2 ,  logging_steps=2 ,  )

							# instantiate trainer
							__lowercase					     =				SeqaSeqTrainer(
							    model=lowerCAmelCase__ ,  args=lowerCAmelCase__ ,  compute_metrics=_compute_metrics ,  train_dataset=lowerCAmelCase__ ,  eval_dataset=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  )

							# start training
							trainer.train()